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Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis.

Soccer is a unique sport where players purposefully and voluntarily use their unprotected heads to manipulate the direction of the ball. There are limited soccer head impact exposure data to further study brain injury risks. The objective of the current study was to combine validated mouthpiece sensors with comprehensive video analysis methods to characterize head impact exposure and biomechanics in university varsity women’s soccer. Thirteen female soccer athletes were instrumented with mouthpiece sensors to record on-field head impacts during practices, scrimmages, and games. Multi-angle video was obtained and reviewed for all on-field activity to verify mouthpiece impacts and identify contact scenarios. We recorded 1307 video-identified intentional heading impacts and 1011 video-verified sensor impacts. On average, athletes experienced 1.83 impacts per athlete-exposure, with higher exposure in practices than games/scrimmages. Median and 95th percentile peak linear and peak angular accelerations were 10.0, 22.2 g, and 765, 2296 rad/s2, respectively. Long kicks, top of the head impacts and jumping headers resulted in the highest impact kinematics. Our results demonstrate the importance of investigating and monitoring head impact exposure during soccer practices, as well as the opportunity to limit high-kinematics impact exposure through heading technique training and reducing certain contact scenarios.

Contact sports players frequently sustain head impacts, most of which are mild impacts exhibiting 10–30 g peak head center-of-gravity (CG) linear acceleration. Wearable head impact sensors are commonly used to measure exposure and typically detect impacts using a linear acceleration threshold. However, linear acceleration across the head can substantially vary during 6-degree-of-freedom motion, leading to triggering biases that depend on sensor location and impact condition. We conducted an analytical investigation with impact characteristics extracted from on-field American football and soccer data. We assumed typical mouthguard sensor locations and evaluated whether simulated multi-directional impacts would trigger recording based on per-axis or resultant acceleration thresholding. Across 1387 impact directions, a 10g peak CG linear acceleration impact would trigger at only 24.7% and 31.8% of directions based on a 10 g per-axis and resultant acceleration threshold, respectively. Anterior impact locations had lower trigger rates and even a 30 g impact would not trigger recording in some directions. Such triggering biases also varied by sensor location and linear-rotational head kinematics coupling. Our results show that linear acceleration-based impact triggering could lead to considerable bias in head impact exposure measurements. We propose a set of recommendations to consider for sensor manufacturers and researchers to mitigate this potential exposure measurement bias.

Although most head impacts in soccer are headers, limited knowledge exists about how header magnitude varies by on-field scenario. This study aimed to compare head kinematics during on-field headers by play state (i.e., corner kick, goal kick, free kick, throw-in, drill, or live ball), intent (i.e., pass, shot, or clearance), and outcome (i.e., successful or unsuccessful). Fifteen female collegiate soccer players were instrumented with mouthpiece-based head impact sensors during 72 practices and 24 games. A total of 336 headers were verified and contextualized via film review. Play state was associated with peak linear acceleration, rotational acceleration, and rotational velocity (all p < .001) while outcome was associated with peak linear acceleration (p < .010). Header intent was not significantly associated with any kinematic metric. Headers during corner kicks (22.9 g, 2189.3 rad/s2, 9.87 rad/s), goal kicks (24.3 g, 2658.9 rad/s2, 10.1 rad/s), free kicks (18.0 g, 1843.3 rad/s2, 8.43 rad/s), and live balls (18.8 g, 1769.7 rad/s2, 8.09 rad/s) each had significantly greater mean peak linear acceleration (all p < .050), rotational acceleration (all p < .001), and rotational velocity (all p < .001) than headers during drills (13.0 g, 982.4 rad/s2, 5.28 rad/s). Headers during goal kicks also had a significantly greater mean rotational acceleration compared to headers during live ball scenarios (p < .050). Successful headers (18.3 g) had a greater mean peak linear acceleration compared to unsuccessful headers (13.8 g; p < .010). Results may help inform efforts to reduce head impact exposure in soccer.

The goal of this paper is to introduce the hypothesis that white matter (WM) and vascular injury are long‐term consequences of repetitive head impacts (RHI) that result in a novel T2 fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging pattern. A non‐systematic literature review of autopsy and FLAIR studies of RHI‐exposed adults was first conducted as a foundation for our hypothesis. A case series of RHI‐exposed participants is presented to illustrate the unique FLAIR WM hyperintensities (WMH) pattern. Current literature shows a direct link between RHI and later‐life WM/vascular neuropathologies, and that FLAIR WMH are associated with RHI, independent of modifiable vascular risk factors. Initial observations suggest a distinctive pattern of WMH in RHI‐exposed participants, termed RHI‐associated WMH (RHI‐WMH). RHI‐WMH defining features are as follows: (1) small, punctate, non‐confluent, (2) spherical, and (3) proximal to the gray matter. Our hypothesis serves as a call for research to empirically validate RHI‐WMH and clarify their biological and clinical correlates. Highlights Repetitive head impacts (RHI) have been associated with later‐life white matter (WM) and vascular neuropathologies. T2 FLAIR MRI of RHI‐exposed participants reveals a potentially unique WM hyperintensity (WMH) pattern that is termed RHI‐associated WMH (RHI‐WMH). RHI‐WMH are characterized as (1) small, punctate, and non‐confluent, (2) spherical, and (3) proximal to the gray matter at an area anatomically susceptible to impact injury, such as the depths of the cortical sulci.

Purpose Wearable sensors are used to measure head impact exposure in sports. The Head Impact Telemetry (HIT) System is a helmet-mounted system that has been commonly utilized to measure head impacts in American football. Advancements in sensor technology have fueled the development of alternative sensor methods such as instrumented mouthguards. The objective of this study was to compare peak magnitude measured from high school football athletes dually instrumented with the HIT System and a mouthpiece-based sensor system. Methods Data was collected at all contact practices and competitions over a single season of spring football. Recorded events were observed and identified on video and paired using event timestamps. Paired events were further stratified by removing mouthpiece events with peak resultant linear acceleration below 10 g and events with contact to the facemask or body of athletes. Results A total of 133 paired events were analyzed in the results. There was a median difference (mouthpiece subtracted from HIT System) in peak resultant linear and rotational acceleration for concurrently measured events of 7.3 g and 189 rad/s 2 . Greater magnitude events resulted in larger kinematic differences between sensors and a Bland Altman analysis found a mean bias of 8.8 g and 104 rad/s 2 , respectively. Conclusion If the mouthpiece-based sensor is considered close to truth, the results of this study are consistent with previous HIT System validation studies indicating low error on average but high scatter across individual events. Future researchers should be mindful of sensor limitations when comparing results collected using varying sensor technologies.

ContextThe importance of analyzing head-impact exposure among football players is well established, but few authors have explored the differences across position groups in high school athletes. Better understanding of these differences may provide optimized intervention strategies for coaches and health care providers.ObjectiveTo compare differences in head-impact characteristics (eg, frequency, severity, and location) among position groups and differences in teams, session types, and seasons across 2 high school varsity football seasons in Hawaii.DesignCohort study.SettingHigh school athletic fields during fall sports seasons.Patients and ParticipantsA total of 200 football players from 3 high school varsity teams, including 69 offensive and defensive linemen; 51 linebackers, running backs, and tight ends; and 80 cornerbacks, safeties, and wide receivers (age = 16.1 ± 0.9 years, height = 177.9 ± 7.8 cm, mass = 86.4 ± 22.7 kg), categorized as linemen, backers, and skill players, respectively.Main Outcome Measure(s)Head impacts per exposure (Imp/E) across positions, teams, session type, and seasons; accumulated total impact burden; and cumulative head-impact burden per location (front, top, right, left, and back) across position groups.ResultsDifferences in Imp/E were found among position groups, with backers (mean = 3.77; 95% CI = 3.146, 4.395) having greater total Imp/E compared with linemen (mean = 1.47; 95% CI = 0.983, 1.96; P < .001) and skill players (mean = 1.56; 95% CI = 1.11, 2.01; P < .001). We observed a difference in total accumulated head-impact burden (F2,194 = 4.938, P < .008), with backers (mean = 4622.85g; 95% CI = 3077.43g, 6168.27g) having a greater total accumulated burden than linemen (mean = 2657.70g; 95% CI = 2045.61g, 3269.19g; P = .01) and skill players (mean = 2875.7g; 95% CI = 2216.38g, 3535.01g; P = .02). Accumulated front impact burden was different among position groups (F2,194 = 7.784, P < .001), with backers (mean = 1606.24g; 95% CI = 977.89g, 2234.58g) having greater accumulated burden than linemen (768.24g; 95% CI = 433.84g, 1102.64g; P = .008) and skill players (567.75g; 95% CI = 360.71g, 774.78g; P < .001).ConclusionsLinebackers, running backs, and tight ends experienced more Imp/E and greater cumulative burden than other positions, which highlights the potential influence of specific positional requirements during football participation. Coaches and health care providers should be aware that players’ positions and roles during play may directly relate to changes in head-impact risk.

The aims of this study were to quantify the sensitivity of various biomechanical measures (linear acceleration, rotational acceleration, impact duration, and impact location) of head impact to the clinical diagnosis of concussion in United States football players and to develop a novel measure of head impact severity combining these measures into a single score that better predicts the incidence of concussion. On-field head impact data were collected from 449 football players at 13 organizations (n = 289,916) using in-helmet systems of six single-axis accelerometers. Concussions were diagnosed by medical staff and later associated with impact data. Principal component analysis and a weighting coefficient based on impact location were used to transform correlated head impact measures into a new composite variable, weighted principal component score (wPCS). The predictive power of linear acceleration, rotational acceleration, head injury criterion, and wPCS was quantified using receiver operating characteristic curves. The null hypothesis, that a measure was no more predictive than guessing, was tested (alpha = 0.05). In addition, receiver operating characteristic curves for wPCS and classical measures were directly compared to test the hypothesis that wPCS was more predictive of concussion than were classic measures (alpha = 0.05). When all of the impacts were considered, every biomechanical measure evaluated was statistically more predictive of concussion than guessing (P < 0.005). However, for the top 1 and 2% of impacts based on linear acceleration, a subset that consisted of 82% of all diagnosed concussions, only wPCS was significantly more predictive of concussion than guessing (P < 0.03); when compared with each other, wPCS was more predictive of concussion than were classical measures for the top 1 and 2% of all of the data (P < 0.04). A weighted combination of several biomechanical inputs, including impact location, is more predictive of concussion than a single biomechanical measure. This study is the first to the authors' knowledge to quantify improvements in the sensitivity of a biomechanical measure to incidence of concussion when impact location is considered.

Athletes participating in contact sports are exposed to repetitive subconcussive head impacts that may have long-term neurological consequences. To better understand these impacts and their effects, head impacts are often measured during football to characterize head impact exposure and estimate injury risk. Despite widespread use of kinematic-based metrics, it remains unclear whether any single metric derived from head kinematics is well-correlated with measurable changes in the brain. This shortcoming has motivated the increasing use of finite element (FE)-based metrics, which quantify local brain deformations. Additionally, quantifying cumulative exposure is of increased interest to examine the relationship to brain changes over time. The current study uses the atlas-based brain model (ABM) to predict the strain response to impacts sustained by 116 youth football athletes and proposes 36 new, or derivative, cumulative strain-based metrics that quantify the combined burden of head impacts over the course of a season. The strain-based metrics developed and evaluated for FE modeling and presented in the current study present potential for improved analytics over existing kinematically-based and cumulative metrics. Additionally, the findings highlight the importance of accounting for directional dependence and expand the techniques to explore spatial distribution of the strain response throughout the brain.

Heading, an integral component of soccer, exposes athletes to a large number of head impacts over a career. The literature has begun to indicate that cumulative exposure may lead to long-term functional and psychological deficits. Quantifying an athlete's exposure over a season is a first step in understanding cumulative exposure.   To measure the frequency and magnitude of direct head impacts in collegiate women's soccer players across impact type, player position, and game or practice scenario.   Cross-sectional study.   National Collegiate Athletic Association Division I institution.   Twenty-three collegiate women's soccer athletes.   Athletes wore Smart Impact Monitor accelerometers during all games and practices. Impacts were classified during visual, on-field monitoring of athletic events. All direct head impacts that exceeded the 10 g threshold were included in the final data analysis. The dependent variable was linear acceleration, and the fixed effects were (1) type of impact: clear, pass, shot, unintentional deflection, or head-to-head contact; (2) field position: goalkeeper, defense, forward, or midfielder; (3) playing scenario: game or practice.   Shots (32.94 g ± 12.91 g, n = 38; P = .02) and clears (31.09 g ± 13.43 g, n = 101; P = .008) resulted in higher mean linear accelerations than passes (26.11 g ± 15.48 g, n = 451). Head-to-head impacts (51.26 g ± 36.61 g, n = 13; P < .001) and unintentional deflections (37.40 g ± 34.41 g, n = 24; P = .002) resulted in higher mean linear accelerations than purposeful headers (ie, shots, clears, and passes). No differences were seen in linear acceleration across player position or playing scenario.   Nonheader impacts, including head-to-head impacts and unintentional deflections, resulted in higher mean linear accelerations than purposeful headers, including shots, clears, and passes, but occurred infrequently on the field. Therefore, these unanticipated impacts may not add substantially to an athlete's cumulative exposure, which is a function of both frequency and magnitude of impact.